{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "567b8c2c",
   "metadata": {},
   "source": [
    "#### 1.5.1 二元数据的数字特征\n",
    "\n",
    "> 示例\n",
    "```\n",
    "某种矿石有两种有用成分A、B，取10个样本，每个样本中成分A的含量百分数x（%）及B的含量百分数y（%）的数据如下。计算样本的均值、方差、协方差和相关系数\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6337bb90",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置字体为楷体\n",
    "plt.rcParams['font.sans-serif'] = ['KaiTi']\n",
    "# 用来正常显示负号\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51bb0e37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "矿石数据：\n",
      " [[67 24]\n",
      " [54 15]\n",
      " [72 23]\n",
      " [64 19]\n",
      " [39 16]\n",
      " [22 11]\n",
      " [58 20]\n",
      " [43 16]\n",
      " [46 17]\n",
      " [34 13]]\n",
      "\n",
      "矿石两种有用成分百分比：\n",
      "      均值\n",
      "x  49.9\n",
      "y  17.4\n",
      "\n",
      "两种成分的协方差矩阵：\n",
      "         x      y\n",
      "x  252.77  60.60\n",
      "y   60.60  17.16\n",
      "\n",
      "两种成分的相关系数矩阵：\n",
      "       x     y\n",
      "x  1.00  0.92\n",
      "y  0.92  1.00\n"
     ]
    }
   ],
   "source": [
    "# 矿石\n",
    "\n",
    "x = np.array([67, 54, 72, 64, 39, 22, 58, 43, 46, 34])\n",
    "y = np.array([24, 15, 23, 19, 16, 11, 20, 16, 17, 13])\n",
    "\n",
    "# 将数据合并，合并成列向量\n",
    "data = np.c_[x, y]  # 此函数比较常用\n",
    "print('矿石数据：\\n', data)\n",
    "\n",
    "# 两种成分的均值，注意 mean函数的axis参数指定计算哪个轴方向，此处为列向量\n",
    "# axis=0 列方向向量\n",
    "print('\\n矿石两种有用成分百分比：\\n', pd.DataFrame(np.mean(data, axis=0),\n",
    "                                      index=['x', 'y'], columns=['均值']))\n",
    "\n",
    "# 协方差矩阵与相关系数矩阵\n",
    "print('\\n两种成分的协方差矩阵：\\n', pd.DataFrame(np.round(np.cov(data.T), 2),\n",
    "                                     index=['x', 'y'], columns=['x', 'y']))\n",
    "print('\\n两种成分的相关系数矩阵：\\n', pd.DataFrame(np.round(np.corrcoef(data.T), 2),\n",
    "                                      index=['x', 'y'], columns=['x', 'y']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eac7b436",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "矿石有用成分之间有无关联性（Pearsonr函数）： (0.9202595441018039, 0.000160515795825038)\n",
      "\n",
      "矿石有用成分之间有无关联性（spearmanr）： SpearmanrResult(correlation=0.8997002046464955, pvalue=0.00039167359562948393)\n",
      "\n",
      "矿石有用成分之间有无关联性（kendalltau）： KendalltauResult(correlation=0.764093177458341, pvalue=0.002263469812035179)\n"
     ]
    }
   ],
   "source": [
    "# 对矿石两种成分之间的相关性检验，分析两种成分之间是否有相关性\n",
    "# 对于数据的检验，在《第三章：假设检验》进行重点讲解\n",
    "\n",
    "# 注意：pearsonr检验的原假设是数据产生自不相关的数据集\n",
    "# 很显然，p值远小于0.05，可以拒绝接受不是产生自同一个数据集的原假设，（接受备择假设，两个样本高度相关）。\n",
    "# 二者之间的相关系数等于：0.92，和前面相关系数矩阵的计算结果一致。\n",
    "# 注意：data的取值，取列向量进行计算。\n",
    "\n",
    "# data[:, 0]  相关系数, data[:, 1]  p值\n",
    "print('矿石有用成分之间有无关联性（Pearsonr函数）：', st.pearsonr(data[:, 0], data[:, 1]))\n",
    "\n",
    "# spearmanr函数无需假设数据服从正态分布，是一种典型的非参数检验，原假设为数据不相关\n",
    "print('\\n矿石有用成分之间有无关联性（spearmanr）：', st.spearmanr(data[:, 0], data[:, 1]))\n",
    "\n",
    "# kendalltau也是一种非参数检验；原假设为数据不相关\n",
    "print('\\n矿石有用成分之间有无关联性（kendalltau）：', st.kendalltau(data[:, 0], data[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "071f3fc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KendalltauResult(correlation=0.3333333333333333, pvalue=0.21637345679012346)\n"
     ]
    }
   ],
   "source": [
    "# 测试两种不同分布的数据是否相关\n",
    "a = st.t.rvs(size=10, df=3)\n",
    "b = st.f.rvs(size=10, dfn=10, dfd=9)\n",
    "\n",
    "# 结果很显然不能拒绝不相关的原假设。p-value>0.05\n",
    "# correlation 相关系数，pvalue p值\n",
    "print(st.kendalltau(a, b))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0bb9375",
   "metadata": {},
   "source": [
    "#### 1.5.2 多元数据的数字特征\n",
    "\n",
    "> 示例：\n",
    "```\n",
    "为了解某种橡胶的性能，抽取10个样品，每个测量三项指标：硬度、变形和弹性，其数据如表。计算样本均值，样本协方差矩阵和样本相关矩阵，并用Pearson相关性检验确认变量X1，X2，X3是否相关？\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56de910f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3项指标的协方差矩阵：\n",
      "       硬度    变形    弹性\n",
      "硬度  4.77 -1.94  1.93\n",
      "变形 -1.94  3.83  0.62\n",
      "弹性  1.93  0.62  6.19\n",
      "\n",
      "3项指标的相关系数矩阵：\n",
      "       硬度    变形    弹性\n",
      "硬度  1.00 -0.45  0.36\n",
      "变形 -0.45  1.00  0.13\n",
      "弹性  0.36  0.13  1.00\n"
     ]
    }
   ],
   "source": [
    "# 多元数据的数字特征\n",
    "\n",
    "# x1，x2，x3分别代表橡胶的硬度、变形和弹性\n",
    "x1 = np.array([65, 70, 70, 69, 66, 67, 68, 72, 66, 68])\n",
    "x2 = np.array([45, 45, 48, 46, 50, 46, 47, 43, 47, 48])\n",
    "x3 = np.array([27.6, 30.7, 31.8, 32.6, 31.0, 31.3, 37.0, 33.6, 33.1, 34.2])\n",
    "data = np.c_[x1, x2, x3]\n",
    "\n",
    "# 协方差矩阵\n",
    "print('3项指标的协方差矩阵：\\n', pd.DataFrame(np.round(np.cov(data.T), 2), \n",
    "                                   index=['硬度', '变形', '弹性'], columns=['硬度', '变形', '弹性']))\n",
    "\n",
    "# 相关系数矩阵\n",
    "print('\\n3项指标的相关系数矩阵：\\n', pd.DataFrame(np.round(np.corrcoef(data.T), 2),\n",
    "                                      index=['硬度', '变形', '弹性'], columns=['硬度', '变形', '弹性']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0cb5d01a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "橡胶的硬度和变形之间有无相关性（pearsonr函数）： (-0.4548831555240914, 0.18653609957909684)\n",
      "\n",
      "橡胶的硬度和弹性之间有无相关性（pearsonr函数）： (0.35612908480948774, 0.3124791340545788)\n",
      "\n",
      "橡胶的变形和弹性之间有无相关性（pearsonr函数）： (0.12659622460695796, 0.7274664789312604)\n"
     ]
    }
   ],
   "source": [
    "print('橡胶的硬度和变形之间有无相关性（pearsonr函数）：', st.pearsonr(data[:, 0], data[:, 1]))\n",
    "print('\\n橡胶的硬度和弹性之间有无相关性（pearsonr函数）：', st.pearsonr(data[:, 0], data[:, 2]))\n",
    "print('\\n橡胶的变形和弹性之间有无相关性（pearsonr函数）：', st.pearsonr(data[:, 1], data[:, 2]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2769d7d",
   "metadata": {},
   "source": [
    "## 1.6 多元数据的基本图形表示\n",
    "\n",
    "> 示例：\n",
    "```\n",
    "为考查学生的学习情况，学校随机的抽取12名学生的5门课期末考试的成绩，如表所示。画出12名学生学习成绩的轮廓图。\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f6c83792",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 35838 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 31243 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 32489 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 36718 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 24275 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 23398 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 29983 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 25919 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 27835 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 35821 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 25991 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 22806 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 29289 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 29702 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 23398 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 29983 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 32489 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 35838 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 31243 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 36718 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 24275 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 25919 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 27835 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 35821 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 25991 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 22806 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 29289 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 29702 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 31665 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 31665 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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R1vNi9zBYFJIWnT/glxdPPUmSmiwKSVKTRSFJarIoJElNFoUkqcmikCQ1WRSSpCaLQpLUtOweM55kFvhB3zmAs4Af9h1iINwXh7kvDnNfHDaEffGrVTU514JlVxRDkWTmWM92Hzfui8PcF4e5Lw4b+r7w1JMkqcmikCQ1WRRLZ3vfAQbEfXGY++Iw98Vhg94XXqOQJDV5RCFJarIoJElNFsUiS/KZJPuT3N93lj4leV2SO5LsTfJAkqv7ztSXJKcnuTvJt7p98fG+M/UtyYok9yb5ct9Z+pTk4ST/keS+JDN95zkWr1EssiTnAz8FbqiqDX3n6UuSNcCaqronySuBPcClVfXtnqONXA593+eZVfXTJKcCdwFXV9XXe47WmyR/BkwBr6qqi/vO05ckDwNTVdX3zXZNHlEssqr6KnCg7xx9q6p9VXVP9/onwF7gtf2m6kcd8tPu7andn7H9DS3J2cB7gE/3nUXzY1FoySVZC7wF+EbPUXrTnWq5D9gP7Kqqsd0XwCeAjwA/6znHEBTwlSR7kmzpO8yxWBRaUkleAXwR+HBV/bjvPH2pquer6hzgbODcJGN5WjLJxcD+qtrTd5aBOK+q3gpcBHywO3U9OBaFlkx3Pv6LwOeq6kt95xmCqvoRcCfwrn6T9OY84L3dufnPA29L8tl+I/Wnqh7rpvuBm4Bz+000N4tCS6K7gLsD2FtVf913nj4lmUzy6u71y4G3Aw/2GqonVfXRqjq7qtYClwP/UlXv6zlWL5Kc2Q30IMmZwDuBQY6WtCgWWZJp4N+ANyZ5NMnmvjP15Dzgjzj0G+N93Z939x2qJ2uAO5L8O/BNDl2jGOthoQJgNXBXkm8BdwO3VtXtPWeak8NjJUlNHlFIkposCklSk0UhSWqyKCRJTRaFJKnplL4DSMtFknOBGxqrvP+lllfV3YubSjpxFoW0eM4A7qyqDxy9IMkn57lcGhxPPUmSmiwKSVKTRSFJarIoJElNFoUkqcmikCQ1WRSSpCaLQpLUZFFIkposCklSk0UhSWryWU/S4nkSuCDJg8dYfsM8lkuD43dmS5KaPPUkSWqyKCRJTRaFJKnJopAkNVkUkqQmi0KS1PT/MLgRUJDrVS8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 学生成绩，Xn代表某一门课所有抽样学生的成绩\n",
    "X1 = [99, 99, 100, 93, 100, 90, 75, 93, 87, 95, 76, 85]   # 政治\n",
    "X2 = [94, 88, 98, 88, 91, 78, 73, 84, 73, 82, 72, 75]  # 语文\n",
    "X3 = [93, 96, 81, 88, 72, 82, 88, 83, 60, 90, 43, 50]  # 外语\n",
    "X4 = [100, 99, 96, 99, 96, 75, 97, 68, 76, 62, 67, 34]  # 数学\n",
    "X5 = [100, 97, 100, 96, 78, 97, 89, 88, 84, 39, 78, 37]  # 物理\n",
    "scores = np.c_[X1, X2, X3, X4, X5]\n",
    "\n",
    "# plt.polar(scores)\n",
    "\n",
    "# 课程的轮廓图\n",
    "plt.figure()\n",
    "plt.xlabel('学生', size=15)\n",
    "plt.ylabel('成绩', size=15)\n",
    "plt.title('课程成绩轮廓图', size=15)\n",
    "\n",
    "# 课程表\n",
    "courses = np.array(['政治', '语文', '外语', '数学', '物理'])\n",
    "\n",
    "# 画出每门课的轮廓线\n",
    "for i in np.arange(0, scores.shape[1]):\n",
    "    # scores[:, i]  列切片\n",
    "    plt.plot(np.arange(1, scores.shape[0]+1), scores[:, i], label=courses[i])\n",
    "    plt.legend()\n",
    "\n",
    "plt.xticks(np.arange(1, scores.shape[0]+1),\n",
    "           ['学生'+str(i+1) for i in np.arange(0, scores.shape[0])])\n",
    "\n",
    "plt.xticks(rotation=45, size=12)\n",
    "plt.yticks(size=12)\n",
    "\n",
    "# 学生轮廓图\n",
    "plt.figure()\n",
    "plt.xlabel('课程', size=15)\n",
    "plt.ylabel('成绩', size=15)\n",
    "plt.title('学生成绩轮廓图', size=15)\n",
    "\n",
    "# 画出每个学生的轮廓线\n",
    "for i in np.arange(0, scores.shape[0]):\n",
    "    # cores[i, :]  行切片\n",
    "    plt.plot(np.arange(1, scores.shape[1]+1), scores[i, :], label='学生'+str(i+1))\n",
    "    plt.legend(loc=3)\n",
    "\n",
    "plt.yticks(size=12)\n",
    "plt.xticks(np.arange(1, scores.shape[1]+1), courses, size=12)\n",
    "\n",
    "# 课程成绩箱线图\n",
    "plt.figure()\n",
    "plt.xlabel('课程', size=15)\n",
    "plt.ylabel('成绩', size=15)\n",
    "plt.title('课程成绩箱线图', size=15)\n",
    "# 以列向量作为对象，即成绩为x轴\n",
    "plt.boxplot(scores)\n",
    "\n",
    "# 学生成绩箱线图\n",
    "plt.figure()\n",
    "plt.xlabel('学生', size=15)\n",
    "plt.ylabel('成绩', size=15)\n",
    "plt.title('学生成绩箱线图', size=15)\n",
    "# 转置之后即以学生为x轴\n",
    "plt.boxplot(scores.T)\n",
    "plt.xticks(np.arange(1, scores.shape[0] + 1))\n",
    "plt.xticks(np.arange(1, scores.shape[0] + 1),\n",
    "          ['学生'+str(i+1) for i in np.arange(0, scores.shape[0])])\n",
    "plt.xticks(rotation=45, size=12)\n",
    "plt.yticks(size=12)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c173ff2f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
